Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	North Dakota	116,384
2	South Dakota	104,677
3	Iowa	82,655
4	Wisconsin	82,124
5	Nebraska	78,867
6	Utah	75,083
7	Rhode Island	71,938
8	Idaho	70,413
9	Montana	69,952
10	Wyoming	69,649
11	Tennessee	69,019
12	Illinois	68,837
13	Minnesota	68,527
14	Kansas	67,930
15	Indiana	65,911
16	Arkansas	63,458
17	Nevada	63,133
18	Mississippi	62,376
19	Alabama	62,334
20	Oklahoma	61,973
21	Missouri	60,939
22	New Mexico	59,307
23	Louisiana	59,272
24	Arizona	59,259
25	Alaska	57,762
26	Florida	53,791
27	Texas	52,949
28	Kentucky	52,422
29	Colorado	51,832
30	Georgia	50,962
31	South Carolina	50,521
32	Ohio	50,026
33	Delaware	49,220
34	Michigan	48,285
35	New Jersey	46,945
36	Connecticut	44,254
37	Massachusetts	44,122
38	California	43,430
39	North Carolina	43,251
40	New York	41,582
41	Pennsylvania	41,024
42	Maryland	40,038
43	West Virginia	37,301
44	District of Columbia	36,276
45	Virginia	34,238
46	Puerto Rico	30,466
47	Washington	28,274
48	New Hampshire	24,588
49	Oregon	23,146
50	Hawaii	13,874
51	Maine	12,878
52	Vermont	9,629

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Rhode Island	1,696
2	Tennessee	1,397
3	California	1,057
4	Kansas	1,057
5	Arizona	1,038
6	Connecticut	1,030
7	Idaho	856
8	Delaware	845
9	Indiana	765
10	Pennsylvania	762
11	Nevada	753
12	New Mexico	724
13	Utah	708
14	Mississippi	693
15	Alabama	680
16	West Virginia	675
17	Massachusetts	654
18	Arkansas	640
19	Oklahoma	636
20	Texas	635
21	Ohio	628
22	New Hampshire	607
23	Nebraska	606
24	Alaska	602
25	South Dakota	590
26	Michigan	587
27	Missouri	584
28	Illinois	575
29	Kentucky	567
30	Georgia	549
31	Wyoming	547
32	New Jersey	542
33	North Carolina	539
34	Wisconsin	527
35	South Carolina	512
36	Colorado	510
37	Louisiana	497
38	New York	481
39	Iowa	479
40	Florida	456
41	Montana	455
42	Minnesota	448
43	Virginia	403
44	Maryland	390
45	North Dakota	356
46	Maine	344
47	District of Columbia	343
48	Oregon	298
49	Washington	259
50	Vermont	136
51	Puerto Rico	130
52	Hawaii	80

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	2,026
2	New York	1,825
3	Massachusetts	1,670
4	North Dakota	1,575
5	Connecticut	1,544
6	Rhode Island	1,500
7	Louisiana	1,491
8	South Dakota	1,469
9	Mississippi	1,442
10	Illinois	1,245
11	Michigan	1,159
12	Iowa	1,063
13	Indiana	1,054
14	Arizona	1,038
15	Pennsylvania	1,031
16	District of Columbia	1,020
17	Arkansas	1,018
18	New Mexico	977
19	Florida	940
20	Georgia	933
21	South Carolina	932
22	Maryland	871
23	Texas	864
24	Nevada	863
25	Alabama	856
26	Delaware	855
27	Minnesota	821
28	Missouri	821
29	Tennessee	821
30	Montana	782
31	Kansas	773
32	Wisconsin	772
33	Nebraska	755
34	Colorado	727
35	Idaho	697
36	Ohio	665
37	West Virginia	579
38	North Carolina	574
39	Kentucky	566
40	Wyoming	566
41	California	553
42	Oklahoma	537
43	Virginia	528
44	New Hampshire	459
45	Washington	412
46	Puerto Rico	410
47	Utah	343
48	Oregon	300
49	Alaska	237
50	Maine	198
51	Hawaii	194
52	Vermont	168

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Rhode Island	25
2	Kansas	20
3	North Dakota	18
4	Iowa	15
5	Missouri	15
6	Pennsylvania	15
7	South Dakota	15
8	Arkansas	14
9	Indiana	14
10	New Mexico	14
11	Connecticut	13
12	Michigan	13
13	West Virginia	13
14	Colorado	12
15	Idaho	12
16	Nebraska	12
17	Nevada	12
18	Illinois	11
19	Mississippi	10
20	Arizona	9
21	New Jersey	9
22	Tennessee	9
23	Louisiana	8
24	Maryland	8
25	Ohio	8
26	Oregon	8
27	Wisconsin	8
28	California	7
29	Massachusetts	7
30	Minnesota	7
31	Alabama	6
32	Texas	6
33	Delaware	5
34	Florida	5
35	Kentucky	5
36	Montana	5
37	New Hampshire	5
38	New York	5
39	North Carolina	5
40	Oklahoma	5
41	Vermont	5
42	Georgia	4
43	Puerto Rico	4
44	Utah	4
45	Washington	4
46	Wyoming	4
47	South Carolina	3
48	Virginia	3
49	Alaska	2
50	District of Columbia	2
51	Maine	2
52	Hawaii	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Crowley	Colorado	241,874	1	99
Norton	Kansas	213,766	2	99
Bon Homme	South Dakota	207,506	3	99
Lincoln	Arkansas	207,002	4	99
Buffalo	South Dakota	205,403	5	99
Davidson	Tennessee	83,326	445	85
Richland	South Carolina	56,153	1478	52
York	South Carolina	45,075	2087	33
Orange	California	35,738	2500	20
Pierce	Washington	26,350	2792	11

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Gove	Kansas	7,587	1	99
Jerauld	South Dakota	7,452	2	99
Dickey	North Dakota	6,568	3	99
Foster	North Dakota	5,919	4	99
Gregory	South Dakota	5,735	5	99
Richland	South Carolina	758	1638	47
Davidson	Tennessee	693	1787	43
York	South Carolina	562	2027	35
Orange	California	541	2076	33
Pierce	Washington	330	2536	19

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.